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dc.contributor.author
Alvarez, Dolores María Eugenia
dc.contributor.author
Balsamo, Nancy Florentina
dc.contributor.author
Modesti, Mario Roberto
dc.contributor.author
Crivello, Mónica Elsie
dc.date.available
2020-05-26T15:47:47Z
dc.date.issued
2019-07
dc.identifier.citation
Alvarez, Dolores María Eugenia; Balsamo, Nancy Florentina; Modesti, Mario Roberto; Crivello, Mónica Elsie; Comparison of neural networks. An estimation model in yield of monoglycerides from biodiesel by-product; EMaTTech Journals; Journal of Engineering Science and Technology Review; 12; 4; 7-2019; 103-107
dc.identifier.issn
1791-2377
dc.identifier.uri
http://hdl.handle.net/11336/105883
dc.description.abstract
Biodiesel is generally manufactured by transesterification, obtaining glycerol as a by-product. The transesterification of methyl stearate selectively produced monoglycerides, for glycerol valuation. Mixed oxides containing lithium catalysed the reaction. The purpose of this work was to develop and compare mathematical models obtained through artificial neural networks (ANN), capable for characterising the relationship between the mole percent conversion of methyl stearate and the yield of the products mono-, di- and triglycerides. The lowest mean squared error (MSE), the highest correlation coefficient (R), similarity in the evolution of validation and simulation errors and absence of data overlearning were considered to select the best model. Three ANNs with backpropagation structures were compared. They evidenced high correspondence between the estimated product yield values and the interpolated experimental ones. The ANN containing 35 neurons with sigmoid transfer function in the hidden layer and a linear neuron in the output one was the simplest.Consequently, the 5, 15 and 60 neurons were also explored in the hidden layer. The ANN structured with an intermediate number of neurons (35) achieved the most adequate MSE, considering mono- and diglyceride products (0.011193, 0.000489). The development of these models contributes to the dynamic estimation of the process.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
EMaTTech Journals
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc/2.5/ar/
dc.subject
ARTIFICIAL NEURAL NETWORK
dc.subject
MONOGLYCERIDES
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YIELD
dc.subject.classification
Ingeniería de Procesos Químicos
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Ingeniería Química
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INGENIERÍAS Y TECNOLOGÍAS
dc.title
Comparison of neural networks. An estimation model in yield of monoglycerides from biodiesel by-product
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2020-05-19T18:31:21Z
dc.journal.volume
12
dc.journal.number
4
dc.journal.pagination
103-107
dc.journal.pais
Grecia
dc.journal.ciudad
Kavala
dc.description.fil
Fil: Alvarez, Dolores María Eugenia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigación y Tecnología Química. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación y Tecnología Química; Argentina
dc.description.fil
Fil: Balsamo, Nancy Florentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigación y Tecnología Química. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación y Tecnología Química; Argentina
dc.description.fil
Fil: Modesti, Mario Roberto. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación en Informática para la Ingeniería; Argentina
dc.description.fil
Fil: Crivello, Mónica Elsie. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Centro de Investigación y Tecnología Química. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación y Tecnología Química; Argentina
dc.journal.title
Journal of Engineering Science and Technology Review
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.25103/jestr.124.12
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://www.jestr.org/downloads/Volume12Issue4/fulltext121242019.pdf
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